Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking experiments with people with and without dyslexia have found differences between populations suggesting that eye movements reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking measures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Ma- chine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.

Categories and Subject Descriptors K.4.2 [Computers and Society]: Social Issues—Assistive technologies for persons with disabilities; I.2.1 [Artificial Intelligence]: Applications and Expert Systems—Medicine and science.

Keywords: Dyslexia, eye tracking, eye movements, diagnosis, detection, prediction, machine learning, support vector machine